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Models of similarity in complex networks

The analysis of networks describing many social, economic, technological, biological and other systems has attracted a lot of attention last decades. Since most of these complex systems evolve over time, there is a need to investigate the changes, which appear in the system, in order to assess the s...

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Autor principal: Shvydun, Sergey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280390/
https://www.ncbi.nlm.nih.gov/pubmed/37346584
http://dx.doi.org/10.7717/peerj-cs.1371
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author Shvydun, Sergey
author_facet Shvydun, Sergey
author_sort Shvydun, Sergey
collection PubMed
description The analysis of networks describing many social, economic, technological, biological and other systems has attracted a lot of attention last decades. Since most of these complex systems evolve over time, there is a need to investigate the changes, which appear in the system, in order to assess the sustainability of the network and to identify stable periods. In the literature, there have been developed a large number of models that measure the similarity among the networks. There also exist some surveys, which consider a limited number of similarity measures and then perform their correlation analysis, discuss their properties or assess their performances on synthetic benchmarks or real networks. The aim of the article is to extend these studies. The article considers 39 graph distance measures and compares them on simple graphs, random graph models and real networks. The author also evaluates the performance of the models in order to identify which of them can be applied to large networks. The results of the study reveal some important aspects of existing similarity models and provide a better understanding of their advantages and disadvantages. The major finding of the work is that many graph similarity measures of different nature are well correlated and that some comprehensive methods are well agreed with simple models. Such information can be used for the choice of appropriate similarity measure as well as for further development of new models for similarity assessment in network structures.
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spelling pubmed-102803902023-06-21 Models of similarity in complex networks Shvydun, Sergey PeerJ Comput Sci Computer Networks and Communications The analysis of networks describing many social, economic, technological, biological and other systems has attracted a lot of attention last decades. Since most of these complex systems evolve over time, there is a need to investigate the changes, which appear in the system, in order to assess the sustainability of the network and to identify stable periods. In the literature, there have been developed a large number of models that measure the similarity among the networks. There also exist some surveys, which consider a limited number of similarity measures and then perform their correlation analysis, discuss their properties or assess their performances on synthetic benchmarks or real networks. The aim of the article is to extend these studies. The article considers 39 graph distance measures and compares them on simple graphs, random graph models and real networks. The author also evaluates the performance of the models in order to identify which of them can be applied to large networks. The results of the study reveal some important aspects of existing similarity models and provide a better understanding of their advantages and disadvantages. The major finding of the work is that many graph similarity measures of different nature are well correlated and that some comprehensive methods are well agreed with simple models. Such information can be used for the choice of appropriate similarity measure as well as for further development of new models for similarity assessment in network structures. PeerJ Inc. 2023-05-02 /pmc/articles/PMC10280390/ /pubmed/37346584 http://dx.doi.org/10.7717/peerj-cs.1371 Text en © 2023 Shvydun https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Computer Networks and Communications
Shvydun, Sergey
Models of similarity in complex networks
title Models of similarity in complex networks
title_full Models of similarity in complex networks
title_fullStr Models of similarity in complex networks
title_full_unstemmed Models of similarity in complex networks
title_short Models of similarity in complex networks
title_sort models of similarity in complex networks
topic Computer Networks and Communications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280390/
https://www.ncbi.nlm.nih.gov/pubmed/37346584
http://dx.doi.org/10.7717/peerj-cs.1371
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